code: def forward(self, h):
      head_outs = [attn_head(h) for attn_head in self.heads]
      if self.merge == "cat":
          # concat on the output feature dimension (dim=1)
          return torch.cat(head_outs, dim=1)
      else:
          # merge using average
          return torch.mean(torch.stack(head_outs))
  
imports: import os
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
  import networkx
  from dgl import DGLGraph
  from dgl.data import citation_graph
  import time
  import numpy
label_desc: Use image node_id 2 for calling the MultiHeadGATLayer obj's underlying member method code with example usage: obj.forward(h) and returns: torch, torch